Dopamine D3 Receptor Ser9Gly and Catechol-O-Methyltransferase Val158Met Polymorphisms and Acute Pain in Sickle Cell Disease
Bibliographic record
Abstract
BACKGROUND: Pain in sickle cell disease (SCD) is characterized by episodes of acute pain, primarily responsible for acute health care utilization, and persistent chronic pain. Pain severity and frequency vary significantly among patients with SCD. In this study, we investigated the possible contribution of monoamine gene polymorphisms to pain variation. METHODS: Adult subjects with SCD completed PAINReportIt, a computerized McGill Pain Questionnaire, from which we calculated the Composite Pain Index. Utilization data were obtained from the medical record and biweekly telephone calls for 12 months. Utilization is defined as admissions to the emergency department and/or the acute care center resulting from a sickle cell pain crisis. We performed genotyping for catechol-O-methyltransferase (COMT) Val158Met (rs4680) and dopamine D3 receptor (DRD3) Ser9Gly (rs6280) polymorphisms, which were analyzed for associations with pain phenotypes. RESULTS: Binary logistic models revealed that DRD3 Ser9Gly heterozygote patients were more likely not to have an acute pain crisis (odds ratio [OR] [95% confidence interval {CI}], 4.37 [1.39-22.89]; P = 0.020), which remained so when demographic variables were considered (OR [95% CI], 4.53 [1.41-28.58]; P = 0.016). COMT Val158Met Met allele showed lower probability for zero utilization (OR [95% CI], 0.32 [0.12-0.83]; P = 0.020) than the Val allele. In the negative binomial regression analysis, subjects with COMT Met/Met genotype had utilization incident rate ratio (95% CI) of 2.20 (1.21-3.99) over those with Val/Val (P = 0.010). CONCLUSIONS: These exploratory findings suggest that DRD3 Ser9Gly and COMT Val158Met may contribute to pain heterogeneity in SCD, as suggested by the different rates of acute pain crisis. Specifically, SCD patients with the DRD3 homozygote genotypes, COMT 158 Met allele or Met/Met genotype, are more likely to have acute care utilization, an indicator of acute pain. These results, however, will need to be further examined in future large prospective studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".